LibMvAvLibrary "LibMvAv"
This library provides a unified interface for calculating a
wide variety of moving averages. It is designed to simplify
indicator development by consolidating numerous MA calculations
into a single function and integrating the weighting
capabilities from the `LibWght` library.
Key Features:
1. **All-in-One MA Function:** The core of the library is the
`ma()` function. Users can select the desired calculation
method via the `MAType` enum, which helps create
cleaner and more maintainable code compared to using
many different `ta.*` or custom functions.
2. **Comprehensive Selection of MA Types:** It provides a
selection of 12 different moving averages, covering
common Pine Script built-ins and their weighted counterparts:
- **Standard MAs:** SMA, EMA, WMA, RMA (Wilder's), HMA (Hull), and
LSMA (Least Squares / Linear Regression).
- **Weighted MAs:** Weight-enhanced versions of the above
(WSMA, WEMA, WWMA, WRMA, WHMA, WLSMA).
3. **Integrated Weighting:** The library provides weighted versions
for each of its standard MA types (e.g., `wsma` alongside `sma`).
By acting as a dispatcher, the `ma()` function allows these
weighted calculations to be called using the optional
`weight` parameter, which are then processed by the `LibWght`
library.
4. **Simple API:** The library internally handles the logic of
choosing the correct function based on the selected `MAType`.
The user only needs to provide the source, length, and
optional weight, simplifying the development process.
---
**DISCLAIMER**
This library is provided "AS IS" and for informational and
educational purposes only. It does not constitute financial,
investment, or trading advice.
The author assumes no liability for any errors, inaccuracies,
or omissions in the code. Using this library to build
trading indicators or strategies is entirely at your own risk.
As a developer using this library, you are solely responsible
for the rigorous testing, validation, and performance of any
scripts you create based on these functions. The author shall
not be held liable for any financial losses incurred directly
or indirectly from the use of this library or any scripts
derived from it.
ma(maType, source, length, weight)
Returns the requested moving average.
Parameters:
maType (simple MAType) : simple MAType Desired type (see enum above).
source (float) : series float Data series to smooth.
length (simple int) : simple int Look-back / period length.
weight (float) : series float Weight series (default = na)
Returns: series float Moving-average value.
Movingavarage
LibWghtLibrary "LibWght"
This is a library of mathematical and statistical functions
designed for quantitative analysis in Pine Script. Its core
principle is the integration of a custom weighting series
(e.g., volume) into a wide array of standard technical
analysis calculations.
Key Capabilities:
1. **Universal Weighting:** All exported functions accept a `weight`
parameter. This allows standard calculations (like moving
averages, RSI, and standard deviation) to be influenced by an
external data series, such as volume or tick count.
2. **Weighted Averages and Indicators:** Includes a comprehensive
collection of weighted functions:
- **Moving Averages:** `wSma`, `wEma`, `wWma`, `wRma` (Wilder's),
`wHma` (Hull), and `wLSma` (Least Squares / Linear Regression).
- **Oscillators & Ranges:** `wRsi`, `wAtr` (Average True Range),
`wTr` (True Range), and `wR` (High-Low Range).
3. **Volatility Decomposition:** Provides functions to decompose
total variance into distinct components for market analysis.
- **Two-Way Decomposition (`wTotVar`):** Separates variance into
**between-bar** (directional) and **within-bar** (noise)
components.
- **Three-Way Decomposition (`wLRTotVar`):** Decomposes variance
relative to a linear regression into **Trend** (explained by
the LR slope), **Residual** (mean-reversion around the
LR line), and **Within-Bar** (noise) components.
- **Local Volatility (`wLRLocTotStdDev`):** Measures the total
"noise" (within-bar + residual) around the trend line.
4. **Weighted Statistics and Regression:** Provides a robust
function for Weighted Linear Regression (`wLinReg`) and a
full suite of related statistical measures:
- **Between-Bar Stats:** `wBtwVar`, `wBtwStdDev`, `wBtwStdErr`.
- **Residual Stats:** `wResVar`, `wResStdDev`, `wResStdErr`.
5. **Fallback Mechanism:** All functions are designed for reliability.
If the total weight over the lookback period is zero (e.g., in
a no-volume period), the algorithms automatically fall back to
their unweighted, uniform-weight equivalents (e.g., `wSma`
becomes a standard `ta.sma`), preventing errors and ensuring
continuous calculation.
---
**DISCLAIMER**
This library is provided "AS IS" and for informational and
educational purposes only. It does not constitute financial,
investment, or trading advice.
The author assumes no liability for any errors, inaccuracies,
or omissions in the code. Using this library to build
trading indicators or strategies is entirely at your own risk.
As a developer using this library, you are solely responsible
for the rigorous testing, validation, and performance of any
scripts you create based on these functions. The author shall
not be held liable for any financial losses incurred directly
or indirectly from the use of this library or any scripts
derived from it.
wSma(source, weight, length)
Weighted Simple Moving Average (linear kernel).
Parameters:
source (float) : series float Data to average.
weight (float) : series float Weight series.
length (int) : series int Look-back length ≥ 1.
Returns: series float Linear-kernel weighted mean; falls back to
the arithmetic mean if Σweight = 0.
wEma(source, weight, length)
Weighted EMA (exponential kernel).
Parameters:
source (float) : series float Data to average.
weight (float) : series float Weight series.
length (simple int) : simple int Look-back length ≥ 1.
Returns: series float Exponential-kernel weighted mean; falls
back to classic EMA if Σweight = 0.
wWma(source, weight, length)
Weighted WMA (linear kernel).
Parameters:
source (float) : series float Data to average.
weight (float) : series float Weight series.
length (int) : series int Look-back length ≥ 1.
Returns: series float Linear-kernel weighted mean; falls back to
classic WMA if Σweight = 0.
wRma(source, weight, length)
Weighted RMA (Wilder kernel, α = 1/len).
Parameters:
source (float) : series float Data to average.
weight (float) : series float Weight series.
length (simple int) : simple int Look-back length ≥ 1.
Returns: series float Wilder-kernel weighted mean; falls back to
classic RMA if Σweight = 0.
wHma(source, weight, length)
Weighted HMA (linear kernel).
Parameters:
source (float) : series float Data to average.
weight (float) : series float Weight series.
length (int) : series int Look-back length ≥ 1.
Returns: series float Linear-kernel weighted mean; falls back to
classic HMA if Σweight = 0.
wRsi(source, weight, length)
Weighted Relative Strength Index.
Parameters:
source (float) : series float Price series.
weight (float) : series float Weight series.
length (simple int) : simple int Look-back length ≥ 1.
Returns: series float Weighted RSI; uniform if Σw = 0.
wAtr(tr, weight, length)
Weighted ATR (Average True Range).
Implemented as WRMA on *true range*.
Parameters:
tr (float) : series float True Range series.
weight (float) : series float Weight series.
length (simple int) : simple int Look-back length ≥ 1.
Returns: series float Weighted ATR; uniform weights if Σw = 0.
wTr(tr, weight, length)
Weighted True Range over a window.
Parameters:
tr (float) : series float True Range series.
weight (float) : series float Weight series.
length (int) : series int Look-back length ≥ 1.
Returns: series float Weighted mean of TR; uniform if Σw = 0.
wR(r, weight, length)
Weighted High-Low Range over a window.
Parameters:
r (float) : series float High-Low per bar.
weight (float) : series float Weight series.
length (int) : series int Look-back length ≥ 1.
Returns: series float Weighted mean of range; uniform if Σw = 0.
wBtwVar(source, weight, length, biased)
Weighted Between Variance (biased/unbiased).
Parameters:
source (float) : series float Data series.
weight (float) : series float Weight series.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population (biased); false → sample.
Returns:
variance series float The calculated between-bar variance (σ²btw), either biased or unbiased.
sumW series float The sum of weights over the lookback period (Σw).
sumW2 series float The sum of squared weights over the lookback period (Σw²).
wBtwStdDev(source, weight, length, biased)
Weighted Between Standard Deviation.
Parameters:
source (float) : series float Data series.
weight (float) : series float Weight series.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population (biased); false → sample.
Returns: series float σbtw uniform if Σw = 0.
wBtwStdErr(source, weight, length, biased)
Weighted Between Standard Error.
Parameters:
source (float) : series float Data series.
weight (float) : series float Weight series.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population (biased); false → sample.
Returns: series float √(σ²btw / N_eff) uniform if Σw = 0.
wTotVar(mu, sigma, weight, length, biased)
Weighted Total Variance (= between-group + within-group).
Useful when each bar represents an aggregate with its own
mean* and pre-estimated σ (e.g., second-level ranges inside a
1-minute bar). Assumes the *weight* series applies to both the
group means and their σ estimates.
Parameters:
mu (float) : series float Group means (e.g., HL2 of 1-second bars).
sigma (float) : series float Pre-estimated σ of each group (same basis).
weight (float) : series float Weight series (volume, ticks, …).
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population (biased); false → sample.
Returns:
varBtw series float The between-bar variance component (σ²btw).
varWtn series float The within-bar variance component (σ²wtn).
sumW series float The sum of weights over the lookback period (Σw).
sumW2 series float The sum of squared weights over the lookback period (Σw²).
wTotStdDev(mu, sigma, weight, length, biased)
Weighted Total Standard Deviation.
Parameters:
mu (float) : series float Group means (e.g., HL2 of 1-second bars).
sigma (float) : series float Pre-estimated σ of each group (same basis).
weight (float) : series float Weight series (volume, ticks, …).
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population (biased); false → sample.
Returns: series float σtot.
wTotStdErr(mu, sigma, weight, length, biased)
Weighted Total Standard Error.
SE = √( total variance / N_eff ) with the same effective sample
size logic as `wster()`.
Parameters:
mu (float) : series float Group means (e.g., HL2 of 1-second bars).
sigma (float) : series float Pre-estimated σ of each group (same basis).
weight (float) : series float Weight series (volume, ticks, …).
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population (biased); false → sample.
Returns: series float √(σ²tot / N_eff).
wLinReg(source, weight, length)
Weighted Linear Regression.
Parameters:
source (float) : series float Data series.
weight (float) : series float Weight series.
length (int) : series int Look-back length ≥ 2.
Returns:
mid series float The estimated value of the regression line at the most recent bar.
slope series float The slope of the regression line.
intercept series float The intercept of the regression line.
wResVar(source, weight, midLine, slope, length, biased)
Weighted Residual Variance.
linear regression – optionally biased (population) or
unbiased (sample).
Parameters:
source (float) : series float Data series.
weight (float) : series float Weighting series (volume, etc.).
midLine (float) : series float Regression value at the last bar.
slope (float) : series float Slope per bar.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population variance (σ²_P), denominator ≈ N_eff.
false → sample variance (σ²_S), denominator ≈ N_eff - 2.
(Adjusts for 2 degrees of freedom lost to the regression).
Returns:
variance series float The calculated residual variance (σ²res), either biased or unbiased.
sumW series float The sum of weights over the lookback period (Σw).
sumW2 series float The sum of squared weights over the lookback period (Σw²).
wResStdDev(source, weight, midLine, slope, length, biased)
Weighted Residual Standard Deviation.
Parameters:
source (float) : series float Data series.
weight (float) : series float Weight series.
midLine (float) : series float Regression value at the last bar.
slope (float) : series float Slope per bar.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population (biased); false → sample.
Returns: series float σres; uniform if Σw = 0.
wResStdErr(source, weight, midLine, slope, length, biased)
Weighted Residual Standard Error.
Parameters:
source (float) : series float Data series.
weight (float) : series float Weight series.
midLine (float) : series float Regression value at the last bar.
slope (float) : series float Slope per bar.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population (biased); false → sample.
Returns: series float √(σ²res / N_eff); uniform if Σw = 0.
wLRTotVar(mu, sigma, weight, midLine, slope, length, biased)
Weighted Linear-Regression Total Variance **around the
window’s weighted mean μ**.
σ²_tot = E_w ⟶ *within-group variance*
+ Var_w ⟶ *residual variance*
+ Var_w ⟶ *trend variance*
where each bar i in the look-back window contributes
m_i = *mean* (e.g. 1-sec HL2)
σ_i = *sigma* (pre-estimated intrabar σ)
w_i = *weight* (volume, ticks, …)
ŷ_i = b₀ + b₁·x (value of the weighted LR line)
r_i = m_i − ŷ_i (orthogonal residual)
Parameters:
mu (float) : series float Per-bar mean m_i.
sigma (float) : series float Pre-estimated σ_i of each bar.
weight (float) : series float Weight series w_i (≥ 0).
midLine (float) : series float Regression value at the latest bar (ŷₙ₋₁).
slope (float) : series float Slope b₁ of the regression line.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population; false → sample.
Returns:
varRes series float The residual variance component (σ²res).
varWtn series float The within-bar variance component (σ²wtn).
varTrd series float The trend variance component (σ²trd), explained by the linear regression.
sumW series float The sum of weights over the lookback period (Σw).
sumW2 series float The sum of squared weights over the lookback period (Σw²).
wLRTotStdDev(mu, sigma, weight, midLine, slope, length, biased)
Weighted Linear-Regression Total Standard Deviation.
Parameters:
mu (float) : series float Per-bar mean m_i.
sigma (float) : series float Pre-estimated σ_i of each bar.
weight (float) : series float Weight series w_i (≥ 0).
midLine (float) : series float Regression value at the latest bar (ŷₙ₋₁).
slope (float) : series float Slope b₁ of the regression line.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population; false → sample.
Returns: series float √(σ²tot).
wLRTotStdErr(mu, sigma, weight, midLine, slope, length, biased)
Weighted Linear-Regression Total Standard Error.
SE = √( σ²_tot / N_eff ) with N_eff = Σw² / Σw² (like in wster()).
Parameters:
mu (float) : series float Per-bar mean m_i.
sigma (float) : series float Pre-estimated σ_i of each bar.
weight (float) : series float Weight series w_i (≥ 0).
midLine (float) : series float Regression value at the latest bar (ŷₙ₋₁).
slope (float) : series float Slope b₁ of the regression line.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population; false → sample.
Returns: series float √((σ²res, σ²wtn, σ²trd) / N_eff).
wLRLocTotStdDev(mu, sigma, weight, midLine, slope, length, biased)
Weighted Linear-Regression Local Total Standard Deviation.
Measures the total "noise" (within-bar + residual) around the trend.
Parameters:
mu (float) : series float Per-bar mean m_i.
sigma (float) : series float Pre-estimated σ_i of each bar.
weight (float) : series float Weight series w_i (≥ 0).
midLine (float) : series float Regression value at the latest bar (ŷₙ₋₁).
slope (float) : series float Slope b₁ of the regression line.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population; false → sample.
Returns: series float √(σ²wtn + σ²res).
wLRLocTotStdErr(mu, sigma, weight, midLine, slope, length, biased)
Weighted Linear-Regression Local Total Standard Error.
Parameters:
mu (float) : series float Per-bar mean m_i.
sigma (float) : series float Pre-estimated σ_i of each bar.
weight (float) : series float Weight series w_i (≥ 0).
midLine (float) : series float Regression value at the latest bar (ŷₙ₋₁).
slope (float) : series float Slope b₁ of the regression line.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population; false → sample.
Returns: series float √((σ²wtn + σ²res) / N_eff).
wLSma(source, weight, length)
Weighted Least Square Moving Average.
Parameters:
source (float) : series float Data series.
weight (float) : series float Weight series.
length (int) : series int Look-back length ≥ 2.
Returns: series float Least square weighted mean. Falls back
to unweighted regression if Σw = 0.
RawCuts_01Library "RawCuts_01"
A collection of functions by:
mutantdog
The majority of these are used within published projects, some useful variants have been included here aswell.
This is volume one consisting mainly of smaller functions, predominantly the filters and standard deviations from Weight Gain 4000.
Also included at the bottom are various snippets of related code for demonstration. These can be copied and adjusted according to your needs.
A full up-to-date table of contents is located at the top of the main script.
WEIGHT GAIN FILTERS
A collection of moving average type filters with adjustable volume weighting.
Based upon the two most common methods of volume weighting.
'Simple' uses the standard method in which a basic VWMA is analogous to SMA.
'Elastic' uses exponential method found in EVWMA which is analogous to RMA.
Volume weighting is applied according to an exponent multiplier of input volume.
0 >> volume^0 (unweighted), 1 >> volume^1 (fully weighted), use float values for intermediate weighting.
Additional volume filter switch for smoothing of outlier events.
DIVA MODULAR DEVIATIONS
A small collection of standard and absolute deviations.
Includes the weightgain functionality as above.
Basic modular functionality for more creative uses.
Optional input (ct) for external central tendency (aka: estimator).
Can be assigned to alternative filter or any float value. Will default to internal filter when no ct input is received.
Some other useful or related functions included at the bottom along with basic demonstration use.
weightgain_sma(src, len, xVol, fVol)
Simple Moving Average (SMA): Weight Gain (Simple Volume).
Parameters:
src (float) : Source input.
len (int) : Length (number of bars).
xVol (float) : Volume exponent multiplier (0 = unweighted, 1 = fully weighted).
fVol (bool) : Volume smoothing filter.
Returns: Standard Simple Moving Average with Simple Weight Gain applied.
weightgain_hsma(src, len, xVol, fVol)
Harmonic Simple Moving Average (hSMA): Weight Gain (Simple Volume).
Parameters:
src (float) : Source input.
len (int) : Length (number of bars).
xVol (float) : Volume exponent multiplier (0 = unweighted, 1 = fully weighted).
fVol (bool) : Volume smoothing filter.
Returns: Harmonic Simple Moving Average with Simple Weight Gain applied.
weightgain_gsma(src, len, xVol, fVol)
Geometric Simple Moving Average (gSMA): Weight Gain (Simple Volume).
Parameters:
src (float) : Source input.
len (int) : Length (number of bars).
xVol (float) : Volume exponent multiplier (0 = unweighted, 1 = fully weighted).
fVol (bool) : Volume smoothing filter.
Returns: Geometric Simple Moving Average with Simple Weight Gain applied.
weightgain_wma(src, len, xVol, fVol)
Linear Weighted Moving Average (WMA): Weight Gain (Simple Volume).
Parameters:
src (float) : Source input.
len (int) : Length (number of bars).
xVol (float) : Volume exponent multiplier (0 = unweighted, 1 = fully weighted).
fVol (bool) : Volume smoothing filter.
Returns: Basic Linear Weighted Moving Average with Simple Weight Gain applied.
weightgain_hma(src, len, xVol, fVol)
Hull Moving Average (HMA): Weight Gain (Simple Volume).
Parameters:
src (float) : Source input.
len (int) : Length (number of bars).
xVol (float) : Volume exponent multiplier (0 = unweighted, 1 = fully weighted).
fVol (bool) : Volume smoothing filter.
Returns: Basic Hull Moving Average with Simple Weight Gain applied.
diva_sd_sma(src, len, xVol, fVol, ct)
Standard Deviation (SD SMA): Diva / Weight Gain (Simple Volume)
Parameters:
src (float) : Source input.
len (int) : Length (number of bars).
xVol (float) : Volume exponent multiplier (0 = unweighted, 1 = fully weighted).
fVol (bool) : Volume smoothing filter.
ct (float) : Central tendency (optional, na = bypass). Internal: weightgain_sma().
Returns:
diva_sd_wma(src, len, xVol, fVol, ct)
Standard Deviation (SD WMA): Diva / Weight Gain (Simple Volume).
Parameters:
src (float) : Source input.
len (int) : Length (number of bars).
xVol (float) : Volume exponent multiplier (0 = unweighted, 1 = fully weighted).
fVol (bool) : Volume smoothing filter.
ct (float) : Central tendency (optional, na = bypass). Internal: weightgain_wma().
Returns:
diva_aad_sma(src, len, xVol, fVol, ct)
Average Absolute Deviation (AAD SMA): Diva / Weight Gain (Simple Volume).
Parameters:
src (float) : Source input.
len (int) : Length (number of bars).
xVol (float) : Volume exponent multiplier (0 = unweighted, 1 = fully weighted).
fVol (bool) : Volume smoothing filter.
ct (float) : Central tendency (optional, na = bypass). Internal: weightgain_sma().
Returns:
diva_aad_wma(src, len, xVol, fVol, ct)
Average Absolute Deviation (AAD WMA): Diva / Weight Gain (Simple Volume) .
Parameters:
src (float) : Source input.
len (int) : Length (number of bars).
xVol (float) : Volume exponent multiplier (0 = unweighted, 1 = fully weighted).
fVol (bool) : Volume smoothing filter.
ct (float) : Central tendency (optional, na = bypass). Internal: weightgain_wma().
Returns:
weightgain_ema(src, len, xVol, fVol)
Exponential Moving Average (EMA): Weight Gain (Elastic Volume).
Parameters:
src (float) : Source input.
len (int) : Length (number of bars).
xVol (float) : Volume exponent multiplier (0 = unweighted, 1 = fully weighted).
fVol (bool) : Volume smoothing filter.
Returns: Exponential Moving Average with Elastic Weight Gain applied.
weightgain_dema(src, len, xVol, fVol)
Double Exponential Moving Average (DEMA): Weight Gain (Elastic Volume).
Parameters:
src (float) : Source input.
len (int) : Length (number of bars).
xVol (float) : Volume exponent multiplier (0 = unweighted, 1 = fully weighted).
fVol (bool) : Volume smoothing filter.
Returns: Double Exponential Moving Average with Elastic Weight Gain applied.
weightgain_tema(src, len, xVol, fVol)
Triple Exponential Moving Average (TEMA): Weight Gain (Elastic Volume).
Parameters:
src (float) : Source input.
len (int) : Length (number of bars).
xVol (float) : Volume exponent multiplier (0 = unweighted, 1 = fully weighted).
fVol (bool) : Volume smoothing filter.
Returns: Triple Exponential Moving Average with Elastic Weight Gain applied.
weightgain_rma(src, len, xVol, fVol)
Rolling Moving Average (RMA): Weight Gain (Elastic Volume).
Parameters:
src (float) : Source input.
len (int) : Length (number of bars).
xVol (float) : Volume exponent multiplier (0 = unweighted, 1 = fully weighted).
fVol (bool) : Volume smoothing filter.
Returns: Rolling Moving Average with Elastic Weight Gain applied.
weightgain_drma(src, len, xVol, fVol)
Double Rolling Moving Average (DRMA): Weight Gain (Elastic Volume).
Parameters:
src (float) : Source input.
len (int) : Length (number of bars).
xVol (float) : Volume exponent multiplier (0 = unweighted, 1 = fully weighted).
fVol (bool) : Volume smoothing filter.
Returns: Double Rolling Moving Average with Elastic Weight Gain applied.
weightgain_trma(src, len, xVol, fVol)
Triple Rolling Moving Average (TRMA): Weight Gain (Elastic Volume).
Parameters:
src (float) : Source input.
len (int) : Length (number of bars).
xVol (float) : Volume exponent multiplier (0 = unweighted, 1 = fully weighted).
fVol (bool) : Volume smoothing filter.
Returns: Triple Rolling Moving Average with Elastic Weight Gain applied.
diva_sd_ema(src, len, xVol, fVol, ct)
Standard Deviation (SD EMA): Diva / Weight Gain: (Elastic Volume).
Parameters:
src (float) : Source input.
len (int) : Length (number of bars).
xVol (float) : Volume exponent multiplier (0 = unweighted, 1 = fully weighted).
fVol (bool) : Volume smoothing filter.
ct (float) : Central tendency (optional, na = bypass). Internal: weightgain_ema().
Returns:
diva_sd_rma(src, len, xVol, fVol, ct)
Standard Deviation (SD RMA): Diva / Weight Gain: (Elastic Volume).
Parameters:
src (float) : Source input.
len (int) : Length (number of bars).
xVol (float) : Volume exponent multiplier (0 = unweighted, 1 = fully weighted).
fVol (bool) : Volume smoothing filter.
ct (float) : Central tendency (optional, na = bypass). Internal: weightgain_rma().
Returns:
weightgain_vidya_rma(src, len, xVol, fVol)
VIDYA v1 RMA base (VIDYA-RMA): Weight Gain (Elastic Volume).
Parameters:
src (float) : Source input.
len (int) : Length (number of bars).
xVol (float) : Volume exponent multiplier (0 = unweighted, 1 = fully weighted).
fVol (bool) : Volume smoothing filter.
Returns: VIDYA v1, RMA base with Elastic Weight Gain applied.
weightgain_vidya_ema(src, len, xVol, fVol)
VIDYA v1 EMA base (VIDYA-EMA): Weight Gain (Elastic Volume).
Parameters:
src (float) : Source input.
len (int) : Length (number of bars).
xVol (float) : Volume exponent multiplier (0 = unweighted, 1 = fully weighted).
fVol (bool) : Volume smoothing filter.
Returns: VIDYA v1, EMA base with Elastic Weight Gain applied.
diva_sd_vidya_rma(src, len, xVol, fVol, ct)
Standard Deviation (SD VIDYA-RMA): Diva / Weight Gain: (Elastic Volume).
Parameters:
src (float) : Source input.
len (int) : Length (number of bars).
xVol (float) : Volume exponent multiplier (0 = unweighted, 1 = fully weighted).
fVol (bool) : Volume smoothing filter.
ct (float) : Central tendency (optional, na = bypass). Internal: weightgain_vidya_rma().
Returns:
diva_sd_vidya_ema(src, len, xVol, fVol, ct)
Standard Deviation (SD VIDYA-EMA): Diva / Weight Gain: (Elastic Volume).
Parameters:
src (float) : Source input.
len (int) : Length (number of bars).
xVol (float) : Volume exponent multiplier (0 = unweighted, 1 = fully weighted).
fVol (bool) : Volume smoothing filter.
ct (float) : Central tendency (optional, na = bypass). Internal: weightgain_vidya_ema().
Returns:
weightgain_sema(src, len, xVol, fVol)
Parameters:
src (float)
len (simple int)
xVol (float)
fVol (bool)
diva_sd_sema(src, len, xVol, fVol)
Parameters:
src (float)
len (simple int)
xVol (float)
fVol (bool)
diva_mad_mm(src, len, ct)
Median Absolute Deviation (MAD MM): Diva (no volume weighting).
Parameters:
src (float) : Source input.
len (int) : Length (number of bars).
ct (float) : Central tendency (optional, na = bypass). Internal: ta.median()
Returns:
source_switch(slct, aux1, aux2, aux3, aux4)
Custom Source Selector/Switch function. Features standard & custom 'weighted' sources with additional aux inputs.
Parameters:
slct (string) : Choose from custom set of string values.
aux1 (float) : Additional input for user-defined source, eg: standard input.source(). Optional, use na to bypass.
aux2 (float) : Additional input for user-defined source, eg: standard input.source(). Optional, use na to bypass.
aux3 (float) : Additional input for user-defined source, eg: standard input.source(). Optional, use na to bypass.
aux4 (float) : Additional input for user-defined source, eg: standard input.source(). Optional, use na to bypass.
Returns: Float value, to be used as src input for other functions.
colour_gradient_ma_div(ma1, ma2, div, bull, bear, mid, mult)
Colour Gradient for plot fill between two moving averages etc, with seperate bull/bear and divergence strength.
Parameters:
ma1 (float) : Input for fast moving average (eg: bullish when above ma2).
ma2 (float) : Input for slow moving average (eg: bullish when below ma1).
div (float) : Input deviation/divergence value used to calculate strength of colour.
bull (color) : Colour when ma1 above ma2.
bear (color) : Colour when ma1 below ma2.
mid (color) : Neutral colour when ma1 = ma2.
mult (int) : Opacity multiplier. 100 = maximum, 0 = transparent.
Returns: Colour with transparency (according to specified inputs)
DynamicFunctionsLibrary "DynamicFunctions"
Custom Dynamic functions that allow an adaptive calculation beginning from the first bar
RoC(src, period)
Dynamic RoC
Parameters:
src (float) : and period
Custom function to calculate the actual period considering non-na source values
period (int)
dynamicMedian(src, length)
Dynamic Median
Parameters:
src (float) : and length
length (int)
kernelRegression(src, bandwidth, kernel_type)
Dynamic Kernel Regression Calculation Uses either of the following inputs for kernel_type: Epanechnikov Logistic Wave
Parameters:
src (float)
bandwidth (int)
kernel_type (string)
waveCalculation(source, bandwidth, width)
Use together with kernelRegression function to get chart applicable band
Parameters:
source (float)
bandwidth (int)
width (float)
Rsi(src, length)
Dynamic RSI function
Parameters:
src (float)
length (int)
dynamicStdev(src, period)
Dynamic SD function
Parameters:
src (float)
period (int)
stdv_bands(src, length, mult)
Dynamic SD Bands
Parameters:
src (float)
length (int)
mult (float)
Returns: Basis, Positive SD, Negative SD
Adx(dilen, adxlen)
Dynamic ADX
Parameters:
dilen (int)
adxlen (int)
Returns: adx
Atr(length)
Dynamic ATR
Parameters:
length (int)
Returns: ATR
Macd(source, fastLength, slowLength, signalSmoothing)
Dynamic MACD
Parameters:
source (float)
fastLength (int)
slowLength (int)
signalSmoothing (int)
Returns: macdLine, signalLine, histogram
two_ma_logicLibrary "two_ma_logic"
The core logic for the two moving average strategy that is used as an example for the internal logic of
the "Template Trailing Strategy" and the "Two MA Signal Indicator"
ma(source, maType, length)
ma - Calculate the moving average of the given source for the given length and type of the average
Parameters:
source (float) : - The source of the values
maType (simple string) : - The type of the moving average
length (simple int) : - The length of the moving average
Returns: - The resulted value of the calculations of the moving average
getDealConditions(drawings, longDealsEnabled, shortDealsEnabled, endDealsEnabled, cnlStartDealsEnabled, cnlEndDealsEnabled, emaFilterEnabled, emaAtrBandEnabled, adxFilterEnabled, adxSmoothing, diLength, adxThreshold)
Parameters:
drawings (TwoMaDrawings)
longDealsEnabled (simple bool)
shortDealsEnabled (simple bool)
endDealsEnabled (simple bool)
cnlStartDealsEnabled (simple bool)
cnlEndDealsEnabled (simple bool)
emaFilterEnabled (simple bool)
emaAtrBandEnabled (simple bool)
adxFilterEnabled (simple bool)
adxSmoothing (simple int)
diLength (simple int)
adxThreshold (simple float)
TwoMaDrawings
Fields:
fastMA (series__float)
slowMA (series__float)
emaLine (series__float)
emaUpperBand (series__float)
emaLowerBand (series__float)
TurntLibraryLibrary "TurntLibrary"
Collection of functions created for simplification/easy referencing. Includes variations of moving averages, length value oscillators, and a few other simple functions based upon HH/LL values.
ma(source, length, type)
Apply a moving average to a float value
Parameters:
source : Value to be used
length : Number of bars to include in calculation
type : Moving average type to use ("SMA","EMA","RMA","WMA","VWAP","SWMA","LRC")
Returns: Smoothed value of initial float value
curve(src, len, lb1, lb2)
Exaggerates curves of a float value designed for use as an exit signal.
Parameters:
src : Initial value to curve
len : Number of bars to include in calculation
lb1 : (Default = 1) First lookback length
lb2 : (Default = 2) Second lookback length
Returns: Curved Average
fragma(src, len, space, str)
Average of a moving average and the previous value of the moving average
Parameters:
src : Initial float value to use
len : Number of bars to include in calculation
space : Lookback integer for second half of average
str : Moving average type to use ("SMA","EMA","RMA","WMA","VWAP","SWMA","LRC")
Returns: Fragmented Average
maxmin(x, y)
Difference of 2 float values, subtracting the lowest from the highest
Parameters:
x : Value 1
y : Value 2
Returns: The +Difference between 2 float values
oscLen(val, type)
Variable Length using a oscillator value and a corresponding slope shape ("Incline",Decline","Peak","Trough")
Parameters:
val : Oscillator Value to use
type : Slope of length curve ("Incline",Decline","Peak","Trough")
Returns: Variable Length Integer
hlAverage(val, smooth, max, min, type, include)
Average of HH,LL with variable lengths based on the slope shape ("Incline","Decline","Trough") value relative to highest and lowest
Parameters:
val : Source Value to use
smooth
max
min
type
include : Add "val" to the averaging process, instead of more weight to highest or lowest value
Returns: Variable Length Average of Highest Lowest "val"
pct(val)
Convert a positive float / price to a percentage of it's highest value on record
Parameters:
val : Value To convert to a percentage of it's highest value ever
Returns: Percentage
hlrange(x, len)
Difference between Highest High and Lowest Low of float value
Parameters:
x : Value to use in calculation
len : Number of bars to include in calculation
Returns: Difference
midpoint(x, len, smooth)
The average value of the float's Highest High and Lowest Low in a number of bars
Parameters:
x : Value to use in calculation
len
smooth : (Default=na) Optional smoothing type to use ("SMA","EMA","RMA","WMA","VWAP","SWMA","LRC")
Returns: Midpoint
